Greece

Reduction of data variables is an important issue and it is needed for the processing of higher dimensional data in the application domains and AI, in which threshold neural networks are extensively used. We develop a reduction of data variables and classification method based on the nearest neighbor relations for threshold networks. First, the nearest neighbor relations are shown to be useful for the generation of threshold functions and Chow parameters. Second, the extended application of the nearest neighbor relations is developed for the reduction of variables based on convex cones. The edges of convex cones are compared for the reduction of variables. Further, hyperplanes with reduced variables are obtained on the convex cones for data classification.

EANN 2020

Reduction of Variables and Generation of Functions through Nearest Neighbor Relations in Threshold Networks

convex cone

nearest neighbor relation

reduction of variables

## Dr. Nishikant (Nish) Sonwalkar: Academician, Scientist, Musician

![](https://assets.underline.io/uploads/markdown_image/1/image/0f9b63a0b3deba4a355d98e2c87af68a.png)

Dr. Sonwalkar is regarded as a leading expert in the application of computers in education. He has extensive research and development experience in hypermedia authoring, mobile learning, adaptive learning, and brain-based synaptic learning, advanced scientific visualization, database management systems, and computer simulation.

After receiving doctoral degree from MIT in the area of Molecular Dynamics of Nano-interfaces, he joined MIT as faculty in Mechanical Engineering, but his passion for technology enabled education led him become the founding director of Hypermedia Teaching Facility (Hypermedia Lab) at MIT and later he was appointed as the Principal Educational Architect of MIT.

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NISH 2021

technical paper

21st edition of EANN (International Conference on Engineering Applications of Neural Networks) should be held in Halkidiki, Greece. Due to current Covid-19 situation the conference will be held virtually and Underline will host a repository of video talks and preserve them for the future. Videos will be released on Underline one day after the conference. 

In the future power system called Smart Grid, power generators using renewable energies will be widely introduced to the power grid and required to balance supply and demand on the grid quickly. Therefore, fast automated demand response (FastADR) that contributes to balance the power grid from demand side through electrical facilities like building air conditioner are focused recently. When electric grid operator will pay incentive to aggregators of the demand side, it is important to estimate accurate baseline load. However, the FastADR must returns quick response by unit of seconds or minute (fine-granularity), therefore it is difficult to estimate baseline load accurately using conventional method. In this research, the baseline load estimation model for air-con time-series data is constructed using long short-term memory (LSTM) neural network, and compared with multilayer perceptron (MLP) neural network model for baseline load estimation. The training and evaluating time-series data is generated by air-con simulator (AE) carried out on the virtual building. In the estimation results using data that were simulated for a month, the average estimation error of the LSTM model is 2.7% and of the MLP model is 5.3%. Therefore, the LSTM model is more effective for baseline estimation than the MLP model. However, data in various situations are required.

LSTM Neural Network for Fine-Granularity Estimation on Baseline Load of Fast Demand Response

Engineering drawings are common across different domains such as Oil \& Gas, construction, mechanical and other domains. Automatic processing and analysis of these drawings is a challenging task. This is partly due to the complexity of these documents and also due to the lack of dataset availability in the public domain that can help push the research in this area. In this paper, we present a multiclass imbalanced dataset for the research community made of 2432 instances of engineering symbols. These symbols were extracted from a collection of complex engineering drawings known as Piping and Instrumentation Diagram (P&ID). By providing such dataset to the research community, we anticipate that this will help attract more attention to an important, yet overlooked industrial problem, and will also advance the research in such important and timely topics. We discuss the datasets characteristics in details, and we also show how Convolutional Neural Networks (CNNs) perform on such extremely imbalanced datasets. Finally, conclusions and future directions are discussed.

Symbols in Engineering Drawings (SiED): An Imbalanced Dataset Benchmarked by Convolutional Neural Networks

Face Recognition (FR) is an important area in computer vision with many applications such as security and automated border controls. The recent advancements in this domain have pushed the per- formance of models to human-level accuracy. However, the varying condi- tions in the real-world expose more challenges for their adoption. In this paper, we investigate the performance of these models. We analyze the performance of a cross-section of face detection and recognition models. Experiments were carried out without any preprocessing on three state- of-the-art face detection methods namely HOG, YOLO and MTCNN, and three recognition models namely, VGGface2, FaceNet and Arcface. Our results indicated that there is a significant reliance by these methods on preprocessing for optimum performance.

Towards a Reliable Face Recognition System

In this paper, we introduce our efficient simple method which can locate all occurrences of pattern P of k subpatterns with “don’t cares” of length m in text S of length n. Our algorithm employs advanced data structure and the Kangaroo method, which can be applied to selected suffixes of the suffix tree of S to answer subsequent queries in O(k) time using a predefined computational method to find all occurrences of pattern P with “don’t cares” in text S in a fast and effective manner.

Detecting Pattern Efficiently with Don’t Cares

Knowledge of permeability, a measure of the ability of rocks to allow fluids to 
flow through them, is essential for building accurate models of oil and gas reservoirs. Permeability is best measured in the laboratory using special core analysis (SCAL), but this is expensive and time-consuming. This is the first major work on predicting permeability in the in the UK Continental Shelf (UKCS) based only on routine
core analysis (RCA) data and a machine-learning approach. We present a
comparative analysis of the various machine learning algorithms and validate
the results, using permeability measured on 273 core samples from
104 wells. Results suggest that machine learning can predict permeability with relatively high accuracy. This opens new research directions in
particular in the oil and gas sector.

Predicting Permeability Based On Core Analysis

Robotic grasping of unknown objects in cluttered scenes is already well established, mainly based on advances in Deep Learning methods. A major drawback is the need for a big amount of real-world training data. Furthermore these networks are not interpretable in a sense that it is not clear why certain grasp attempts fail. To make the process of robotic grasping traceable and simplify the overall model we suggest to divide the complex task of robotic grasping into three simpler tasks to find stable grasp points. The first task is to find all grasp points where the gripper can be lowered onto the table without colliding with the object. The second task is to determine for the grasp points and gripper parameters from the first step how the object moves while the gripper is closed. Finally in the third step for all grasp points from the second step it is predicted whether the object slips out of the gripper during lifting. By this simplification it is possible to understand for each grasp point why it is stable and - just as important - why others are unstable or not feasible. In this study we focus on the second task, the prediction of the physical interaction between gripper and object while the gripper is closed. We investigate different Convolutional Neural Network (CNN) architectures and identify the architecture(s) that predict the physical interactions in image space best. We perform the experiments for training data generation in the robot and physics simulator V-REP.

Visual Movement Prediction for Stable Grasp Point Prediction

The aim of the present study is to investigate the separation abilities of three statistical parameters for grouping participants in the visual-motor experiment by their age and gender. These parameters represent different characteristics of the decision-making process and were determined by applying the hierarchical drift diffusion model to the response time and accuracy of the experimental data [1]. The objective function cluster analysis was applied to explore distinct data spaces formed by the parameters’ data. The ability for grouping is assessed and interpreted according to the differences in the subjects’ capabilities to perform the visuo-motor task. The study compares the conclusions based by drift-diffusion model using Bayesian parameter estimation with those based on the cluster analysis in terms of ability to distinguish the performance of different age groups. The investigation of gender effects are uniquely investigated by cluster analysis technique.

Eye Movement Data Analysis

This paper presents a Data Science-oriented application for image classification tasks that is able to automatically: a) gather images needed for training Deep Learning (DL) models with a built-in search engine crawler; b) remove duplicate images; c) sort images using built-in pre-trained DL models or user’s own trained DL model; d) apply data augmentation; e) train a DL classification model; f) evaluate the performance of a DL model and system by using an accuracy calculator as well as the Accuracy Per Consumption (APC), Accuracy Per Energy Cost (APEC), Time to closest APC (TTCAPC) and Time to closest APEC (TTCAPEC) metrics calculators. Experimental results show that the proposed Computer Vision application has several unique features and advantages, proving to be efficient regarding execution time and much easier to use when compared to similar applications.

Deep Learning-Based Computer Vision Application with Multiple Built-In Data Science-Oriented Capabilities

Metalearning is a methodology aiming at recommending the most suitable algorithm (or method) from several alternatives for a par-
ticular dataset. Its classification rule is learned over an available training database of datasets. It gradually penetrates to various applications in
computer science and has also the potential to recommend the most suitable statistical estimator for a given dataset. We consider the non-linear regression model. While there are some robust alternatives to the
traditional (and very non-robust) nonlinear least squares available, it is not theoretically known which estimator performs the best for a partic-
ular dataset. In this work, we perform a metalearning study performed over 721 datasets predicting the best nonlinear regression estimator for an independent dataset. The estimators considered here include stan-
dard nonlinear least squares as well as its robust alternatives with a high breakdown point. On the whole, the presented study brings new argu-
ments in favor of the nonlinear least weighted squares estimator, which is based on the idea to assign implicit weights to individual observations
based on outlyingness of their residuals.

A metalearning study for nonlinear robust regression

This presentation shows the application CNNs in semantic segmentation of vineyard images

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